Demonstrating the method with a uniform base distribution is significant because it can be generalized to model permutationally invariant distributions, which has been a highlight in prior literature. Furthermore, training with a flat base distribution generates flows that can be easily composed backto-back to connect any pair of ensembles of the same dimensionality, thus opening up the possibility of connecting different packings to a common base distribution. This, in turn, enables reweighting with Multistate BAR, as discussed in Ref. and demonstrated for isolated molecules in Ref. . Furthermore, mapping across packing polymorphs via a flat base distribution enables training a number of maps equal to the number of metastable states, thus scaling favorably with the number of polymorphs investigated and setting the scene for large-scale applications of free energy methods based on PGMs. The results illustrated here show that mapping via a flat base distribution is not only possible but also accurate and computationally cheaper than ground truth with the whole range of temperature conditions of practical relevance for finite-temperature crystal structure prediction.