The design of multi-stable RNA molecules has important applications in biology, medicine, and biotechnology. Synthetic design approaches profit strongly from effective in-silico methods, which can tremendously impact their cost and feasibility. We revisit a central ingredient of most in-silico design methods: the sampling of sequences for the design of multi-target structures, possibly including pseudoknots. For this task, we present the efficient, tree decomposition-based algorithm \ourprog{}. Our fixed parameter tractable approach is underpinned by establishing the $\#${\sf P}-hardness of uniform sampling. Modeling the problem as a constraint network, \ourprog{} supports generic Boltzmann-weighted sampling for arbitrary additive RNA energy models; this enables the generation of RNA sequences meeting specific goals like expected free energies or \GCb-content. Finally, we empirically study general properties of the approach and generate biologically relevant multi-target Boltzmann-weighted designs for a common design benchmark. Generating seed sequences with \ourprog{}, we demonstrate significant improvements over the previously best multi-target sampling strategy (uniform sampling).Our software is freely available at: https://github.com/yannponty/RNARedPrint .