How do we know any problem is in NP-complete if we don't know all problems in NP?
We do know all problems in NP. Each problem in NP is given by a non-deterministic Turing machine running in polynomial time. Steve Cook (and, independently, Leonid Levin) proved that SAT is NP-complete by encoding the statement "Machine $M$ accepts $x$ given non-deterministic choices $y$" as a logical formula for every non-deterministic Turing machine; for a machine running in time $T$ and space $S$, the encoding has length roughly $O(TS)$, so when $M$ is polynomial time, the encoding has polynomial length. The corresponding SAT formula states that "for some $y$, machine $M$ accepts $x$ given non-deterministic choices $y$". This formula is satisfiable iff $M$ accepts $x$.

Having proven that one problem is NP-complete, there is no need to do this encoding again. To prove that some other problem $L$ is NP-hard, it is enough to reduce SAT to $L$. Every problem in NP is reducible to SAT, and via the auxiliary reduction, to $L$. This is the way NP-hardness results are proved nowadays, by reducing from some NP-hard problem.