A promising pathway to achieving the required high level accuracy of organic CSP at low cost is through the use of machine learned potentials (MLPs) which, following training on ab initio reference data, can estimate energies with the same level of accuracy but at a fraction of the cost. Recent developments in both theory and computing hardware have led to MLPs being widely adopted across materials modelling, including in CSP. Their adoption for organic CSP, however, has been slower than other areas due to the unique challenges of these materials. Moreover, modern MLPs in general rely on an axiom that the total energy can be decomposed into a sum of atomic energies, which are predicted based on local atomic environment descriptors. This particularly works well when the interaction is well-screened beyond the cutoff radius or when the bonding is homogeneous, as in inorganic materials. However, it cannot accurately capture interactions that occur on different scales, including the diversity of intermolecular interactions in organic crystals. Hence, while modern MLPs excel at describing short range interactions, they often completely neglect the longer range interactions, including long-range dispersion and electrostatics, that extend beyond typical, computationally feasible cutoffs for the local atomic environment. Overcoming this limitation in order to capture all the relevant interactions in organic crystals requires either a more complex MLP method , or alternatively incorporation of a physical baseline that incorporates the missing long-range interactions.