The workflow developed here is applied to a pre-computed CSP landscape. Therefore, the methodology can be applied to existing legacy or published landscapes, as well as new CSP studies. However, the requirement for a pre-computed CSP structure set means that a 'good enough' baseline model is required. Where the baseline is an empirically parameterized force field, molecules with less common functional groups or elements might be less well modelled by common empirical force fields; therefore, either developing tailored force field parameters or using higher level and likely more expensive methods, such as DFTB, might still be required to generate the initial landscape. A further consideration is that the potentials generated with the methods presented here are reliable at local minima on the lattice energy surface and, when on-the-fly training to Monte Carlo trajectories is included, are accurate in the local region of the lattice energy surface. Thus, the potentials could be expected to perform well for lattice energy minimization from good starting structures, as well as for properties such as elastic constants and phonons. However, properties and behaviour that requires a broader description of the lattice energy surface, such as transitions between polymorphs, might require the potential to extrapolate beyond its training, so risks loss of accuracy.