Initialise zarr metadata without computing dask graph

Is your feature request related to a problem? Please describe.
On writing large zarr stores, the xarray docs recommend first creating an initial Zarr store without writing all of its array data. The recommended approach is to first create a dummy dask-backed Dataset, and then call to_zarr with compute=False to write only metadata to Zarr. This works great.

It seems that in one common use case for this approach (including the example in the above docs), the entire dataset to be written to zarr is already represented in a Dataset (let's call this ds). Thus, rather than creating a dummy Dataset with exactly the same metadata as ds, it is more convenient to initialise the zarr Store with ds.to_zarr(..., compute=False). See for example:

https://discourse.pangeo.io/t/many-netcdf-to-single-zarr-store-using-concurrent-futures/2029
https://discourse.pangeo.io/t/map-blocks-and-to-zarr-region/2019
https://discourse.pangeo.io/t/netcdf-to-zarr-best-practices/1119/12
https://discourse.pangeo.io/t/best-practice-for-memory-management-to-iteratively-write-a-large-dataset-with-xarray/1989

However, calling to_zarr with compute=False still computes the dask graph for writing the Zarr store. The graph is never used in this use-case, but computing the graph can take a really long time for large graphs.

Describe the solution you'd like
Is there scope to add an option to to_zarr to initialise the store without computing the dask graph? Or perhaps an initialise_zarr method would be cleaner?

Start with the ds.to_zarr(..., compute=False) entry point and the linked xarray I/O documentation, then compare the behavior with the cited Pangeo use cases. Determine how store metadata can be initialized without computing the Dask graph; done means a supported API achieves that behavior while preserving normal to_zarr operation.