Free energy estimates are key to quantitatively understanding physical-chemical processes ranging from solubility to binding affinity. In the case of polymorphism of molecular crystals in particular, accurate and efficient lattice free energies are needed to determine the relative stability of crystal packings, which is important in many industries, including chemicals, pharmaceuticals, semiconductors, and food products. Predicting the temperature dependence of relative stability of polymorphs requires more thermodynamic information than zero-Kelvin rankings using potential energy calculations to the finite-temperature thermodynamic ranking of polymorphs. Including such thermodynamic detail is often prohibitively expensive due to the anharmonic nature and the presence of low-frequency modes in the vibrational free energy. For free energy calculations to be of industrial relevance, free energy methods are needed that are suitably cheap and scalable for deployment on larger datasets of putative finite temperature polymorphs while retaining accuracy levels on par with states of the art classical anharmonic free energy methods. . This is especially true of systems with increasing size and complexity and for any calculations used in earlier stage, higher throughput screening, Given an atomistic model of the system of interest and a putative crystal structure, its finite-temperature lattice free energy can be rigorously computed using statistical mechanics computational techniques, often based on the principles of free energy perturbation and thermodynamic integration. Several approaches have been proposed in the literature, including various implementations of the Einstein crystal method , diabat approaches drawing inspiration from latticeswitch Monte Carlo and Marcus theory and enhanced sampling methods based on introducing biasing potentials , to name a few. While these approaches are theoretically well established and formally yield fully anharmonic lattice-free energies, their computational cost is frequently too demanding to enable a large-scale deployment. For this reason, the current state of the field relies on the ad hoc application of exact methods such as the Einstein crystal and the more widespread use of methods based on the harmonic approximation, which are nowadays routinely deployed to carry out a final refinement of zero-Kelvin energy landscapes. One of the main limitations of formally exact methods based on statistical mechanics is that they typically require a significant amount of computing effort to sample intermediate, unphysical states, which increases the computational cost without providing inherent physical insight. Taking the Einstein crystal method as the "gold standard" example of a physics-based, formally exact approach for calculating lattice-free energies, we note that its convergence requires devising a chain of states with overlapping configurational distributions that connect the Hamiltonian describing the physical crystal polymorph to the Einstein crystal Hamiltonian.