  Genarris is an open-source Python package for generating random molecular
crystal structures with physical constraints for seeding crystal structure
prediction algorithms and training machine learning models. Here we present a
new version of the code, containing several major improvements. An MPI-based
parallelization scheme has been implemented, which facilitates the seamless
sequential execution of user-defined workflows. A new method for estimating the
unit cell volume based on the single molecule structure has been developed
using a machine-learned model trained on experimental structures. A new
algorithm has been implemented for generating crystal structures with molecules
occupying special Wyckoff positions. A new hierarchical structure check
procedure has been developed to detect unphysical close contacts efficiently
and accurately. New intermolecular distance settings have been implemented for
strong hydrogen bonds. To demonstrate these new features, we study two specific
cases: benzene and glycine. For all polymorphs, the final pool either contained
the experimental structure, or structures with similar lattice parameters,
symmetry, and packing motifs.
